An ability to efficiently and robustly track objects in video is an important function of a surveillance system. Occlusion is one of the most significant challenges in successfully detecting and tracking moving objects. Many approaches have been proposed for tracking objects in video and addressing occlusions. One approach combined a grey-scale texture appearance and shape information to track people in an outdoor environment, while assuming that the whole body of each person is visible for groups of people. Another approach tracked multiple people in the presence of occlusions by using a temporal color-based appearance model with associated weights that are determined by the size, duration, frequency, and adjacency of the object. Another approach employs an appearance model to segment objects during partial occlusions and resolve depth ordering of objects in completed occlusions. Still another approach uses a non real-time tracking system to track human interactions for indoor environments based on a color correlogram and assumes that the humans are relatively large. In this approach, to build the color model, it is assumed that each foreground blob corresponds to only one person when the track begins. Yet another approach uses a very simple multiple-hypotheses based tracking method based on the foreground blobs.
Several approaches use a background subtraction (BGS) solution in order to initially detect moving objects. To this extent, imperfect BGS is a common problem in object tracking. In particular, due to limited resolution of the image and/or limits in the BGS solution, an object may be split into multiple “blobs” (i.e., spatial fragmentation) or disappear for one or more frames (i.e., temporal fragmentation) after BGS is performed on the image.